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Conference Paper: Content-Based Compressive Sensing

TitleContent-Based Compressive Sensing
Authors
Li, CCheng, YSun, ZHe, PBi, SXi, N
Issue Date2018
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1800486
Citation
Proceedings of 2018 IEEE 8th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), Tianjin, China, 19-23 July 2018, p. 379-384 How to Cite?
DOI: http://dx.doi.org/10.1109/CYBER.2018.8688211
AbstractCompressive sensing (CS)provides an alternative to Shannon/Nyquist sampling theorem by performing signal acquisition and compression simultaneously when the signals are sparse or compressible in certain basis. However, the classical random CS strategy heavily relies on the sparsity of signals under acquisition and fails to reconstruct the signals when they are not sparse. In this paper, we propose an approach to sensing signals based on their content information to reduce the sensing rate and get rid of the sparsity requirement. Experimental results demonstrate the effectiveness of the proposed method for both sparse and non-sparse images.
Persistent Identifierhttp://hdl.handle.net/10722/273051
ISSN
2379-7711

 

DC FieldValueLanguage
dc.contributor.authorLi, C-
dc.contributor.authorCheng, Y-
dc.contributor.authorSun, Z-
dc.contributor.authorHe, P-
dc.contributor.authorBi, S-
dc.contributor.authorXi, N-
dc.date.accessioned2019-08-06T09:21:38Z-
dc.date.available2019-08-06T09:21:38Z-
dc.date.issued2018-
dc.identifier.citationProceedings of 2018 IEEE 8th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), Tianjin, China, 19-23 July 2018, p. 379-384-
dc.identifier.issn2379-7711-
dc.identifier.urihttp://hdl.handle.net/10722/273051-
dc.description.abstractCompressive sensing (CS)provides an alternative to Shannon/Nyquist sampling theorem by performing signal acquisition and compression simultaneously when the signals are sparse or compressible in certain basis. However, the classical random CS strategy heavily relies on the sparsity of signals under acquisition and fails to reconstruct the signals when they are not sparse. In this paper, we propose an approach to sensing signals based on their content information to reduce the sensing rate and get rid of the sparsity requirement. Experimental results demonstrate the effectiveness of the proposed method for both sparse and non-sparse images.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1800486-
dc.relation.ispartofIEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)-
dc.rightsIEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER). Copyright © IEEE.-
dc.rights©2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.titleContent-Based Compressive Sensing-
dc.typeConference_Paper-
dc.identifier.emailSun, Z: sunzy@hku.hk-
dc.identifier.emailBi, S: shengbi@hku.hk-
dc.identifier.emailXi, N: xining@hku.hk-
dc.identifier.authorityXi, N=rp02044-
dc.identifier.doi10.1109/CYBER.2018.8688211-
dc.identifier.scopuseid_2-s2.0-85064964930-
dc.identifier.hkuros300619-
dc.identifier.spage379-
dc.identifier.epage384-
dc.publisher.placeUnited States-
dc.identifier.issnl2379-7711-

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